The amount of data stored, processed, and utilized across business industries is increasing at an exponential rate. Along with the increase in volume, the data that is embedded in business documents is growing in complexity at an equally significant rate. Yet, business-processing programs utilized today are unable to keep pace with the business demands of many companies to detect, properly extract and process the data from documents in an efficient, accurate, and cost-effective manner.
While a completely digital business environment has been realized for many companies in particular industries—where, for example, emails and digital copies of documents, forms, and other files are completely relied upon in daily operations—this is simply not possible for many companies, especially those operating in the financial, legal and medical industries. Indeed, due to business guidelines, regulatory requirements, or simply due to a hesitancy to stray from the familiar business practices, most companies rely on both digital and physical documents. For example, in the financial industry, many financial disclosure documents must be filed in physical form. But these documents must also be converted into digital form. Doing so allows companies to analyze data contained in these documents efficiently and with lower costs. However, particularly with documents containing complex and voluminous data sets, performing the detection and extraction of important data from physical or scanned documents has become a complicated and time-consuming process.
Currently available software solutions fail to effectively and efficiently process these complex documents. Within large documents containing unstructured data, the information and data fields that are of interest are often inconsistently referenced or scattered throughout the document, making it virtually impossible to automate the data extraction process. Traditional methods rely on time tested data entry techniques where an individual is responsible for transcribing data from a source document into a desired electronic format. However, this method is inaccurate. For instance, even if a proficient data entry specialist enters the data, the results may still be prone to human error from time-to-time. There is simply no adequate solution available. Indeed, there is currently no available system that allows users to quickly and accurately enter data from large, unstructured datasets.
Accordingly, there is an important need for an improved method and system for processing documents and files to detect and extract relevant embedded data according to business rules and needs. The solution should overcome the aforementioned deficiencies and others.